Pyspark throwing error while trying to read parquet

Question

I am a newbie in pyspark, While trying to read parquet file through pyspark I get the below error. I have tried various things like reinstallation of jre and jdk, updating environment variables. Matching winutils.exe but nothing seems to work.

Error: **

An error occurred while calling o60.load.
: java.lang.UnsatisfiedLinkError: org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Ljava/lang/String;I)Z
    at org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Native Method)
    at org.apache.hadoop.io.nativeio.NativeIO$Windows.access(NativeIO.java:793)
    at org.apache.hadoop.fs.FileUtil.canRead(FileUtil.java:1215)
    at org.apache.hadoop.fs.FileUtil.list(FileUtil.java:1420)
    at org.apache.hadoop.fs.RawLocalFileSystem.listStatus(RawLocalFileSystem.java:601)
    at org.apache.hadoop.fs.FileSystem.listStatus(FileSystem.java:1972)
    at org.apache.hadoop.fs.FileSystem.listStatus(FileSystem.java:2014)
    at org.apache.hadoop.fs.ChecksumFileSystem.listStatus(ChecksumFileSystem.java:761)
    at org.apache.spark.util.HadoopFSUtils$.listLeafFiles(HadoopFSUtils.scala:225)
    at org.apache.spark.util.HadoopFSUtils$.$anonfun$parallelListLeafFilesInternal$1(HadoopFSUtils.scala:95)
    at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:286)
    at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
    at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
    at scala.collection.TraversableLike.map(TraversableLike.scala:286)
    at scala.collection.TraversableLike.map$(TraversableLike.scala:279)
    at scala.collection.AbstractTraversable.map(Traversable.scala:108)
    at org.apache.spark.util.HadoopFSUtils$.parallelListLeafFilesInternal(HadoopFSUtils.scala:85)
    at org.apache.spark.util.HadoopFSUtils$.parallelListLeafFiles(HadoopFSUtils.scala:69)
    at org.apache.spark.sql.execution.datasources.InMemoryFileIndex$.bulkListLeafFiles(InMemoryFileIndex.scala:158)
    at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.listLeafFiles(InMemoryFileIndex.scala:131)
    at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.refresh0(InMemoryFileIndex.scala:94)
    at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.<init>(InMemoryFileIndex.scala:66)
    at org.apache.spark.sql.execution.datasources.DataSource.createInMemoryFileIndex(DataSource.scala:565)
    at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:409)
    at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:274)
    at org.apache.spark.sql.DataFrameReader.$anonfun$load$3(DataFrameReader.scala:245)
    at scala.Option.getOrElse(Option.scala:189)
    at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:245)
    at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:188)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)
    at py4j.ClientServerConnection.run(ClientServerConnection.java:106)
    at java.lang.Thread.run(Thread.java:750)

**

Edit - code is quite simple:-

python
import pyspark.sql.functions as F
import pandas as pd
from pyspark.sql import SparkSession
from pyspark.sql.types import TimestampType
from pyspark.sql.utils import AnalysisException

spark = SparkSession.builder.getOrCreate()

path = "C:\\ProgramData\\MyData"

df = spark.read.format("parquet").option("header",True).load(str(path))

I am using spark-3.2.1-bin-hadoop3.2. Python version is 3.10.

Solution:- Copy winutils from link and try one by one version and check which version is working. Ideally the version matching with Pyspark version should work but different version worked in my case.

Please help out if you get any clue.